用 ML 标注数据训练模型：一阶与零阶随机优化方法
Training with ML-labeled Data via Stochastic First- and Zeroth-Order Optimization
标注太贵？这篇论文教你只用一部分人工标注，剩下的靠模型打标签也能训练，从线性回归到 LLM 微调都验证了。
一篇 arXiv 论文针对标注成本高的问题，提出在部分样本有真值标注、其余由 ML 预测器标注（可能含错误标签）的数据集上训练的优化方法。论文给出 first-order 和 zeroth-order 两类随机优化算法，专门处理混合标注数据中的噪声标签。实验覆盖从 linear regression 训练到 LLM 微调的场景。结果表明这些算法能利用 ML 标注数据有效训练，减少对人工标注的依赖。
Training with ML-labeled Data via Stochastic First- and Zeroth-Order Optimization
Training a machine learning (ML) model is an optimization process that typically requires a set of examples with ground-truth labels beforehand. Obtaining these ground truths usually requires laborious human effort and can be costly. The scalability of human labeling becomes a concern when there are a large number of data points that need to be labeled. To address this practical issue, we consider an optimization setting where a subset of samples has ground-truth labels, while the remaining data points were labeled by an ML predictor, which could potentially assign incorrect labels to those points. We propose both first-order and zeroth-order stochastic optimization methods for training on datasets consisting of a mix of true-labeled and ML-labeled samples. We further conduct comprehensive empirical studies from training a linear regression model to fine-tuning a large language model (LLM) to showcase the effectiveness of the proposed algorithms for training with ML-labeled data.